That need may be acquiring of a new data source, changes to an existing source, or decision-makers’ demands for insights into business questions. A business need triggers pipeline development.

Traditional ETL processes are multi-stage operations that ensure data entering a repository consistently meets governance and quality standards. Data scientists and business intelligence analysts can transform this data on demand to meet the requirements of their analytics projects while leaving the original data intact. Leaving data in its natural state creates optionality for data-driven organizations. Standardization with ETL pipelines makes data analysis more accessible to non-technical and expert users.ĮLT is a more recent method for transferring structured, semi-structured, and unstructured data from sources to the raw data store of a data lake. ELT comparison comes down to how these pipelines handle data and the data management requirements driving their development.ĮTL is an established method for transferring primarily structured data from sources and processing the data to meet a data warehouse’s schema, format, and other requirements. Wading in a little deeper than superficial name differences, the ETL vs. This is why data engineers build ELT pipelines to fill their data lakes. Since lakes store raw data, data pipelines did not need to transform data upon ingestion, leaving the data processing to each use case. In addition, lakes can store more diverse data types in their raw formats. Able to leverage commodity, cloud-based data storage, lakes are more scalable and affordable. Data warehouses lacked the flexibility and scalability to handle the ever-growing volumes, velocities, and complexities of enterprise data. Over time, a rich ecosystem of ETL tools arose to streamline and automate data warehouse ingestion.Īs the era of big data emerged, this architecture began to show its weaknesses. Data engineers had to pull information from the company’s diverse sources and impose consistency to make the data warehouse useful.

Data became less accessible, requiring considerable time, effort, and expense to generate valuable insights about the business.ĭata warehousing attempted to resolve this situation by creating a central repository from which data analysts could easily discover and use data to support decision-makers.

Moreover, inconsistent governance allowed domains to build their data systems to varying quality standards, schema, format, and other criteria. Each database management system formatted data differently, used different querying methods, and did not share data easily. Evolution from ETL to ELTĪs enterprise data sources proliferated, information architectures fragmented into isolated data silos. ELT pipelines are routinely used to feed raw data into data lake platforms like Microsoft’s Azure Data Lake. These pipelines extract and load data into the target system, after which transformation occurs. What is ELT?ĮLT pipelines serve the same purpose as ETL pipelines but execute the steps in a different order. This guide will compare and contrast these data integration methods, explain their use cases, and discuss how data virtualization can streamline pipeline development and management - or replace pipelines altogether. Load: The loading process deposits data, whether processed or not, into its final data store, where it will be accessible to other systems or end users, depending on its purpose.Ĭommonly used to populate a central data repository like a data warehouse or a data lake, ETL and ELT pipelines have become essential tools for supporting data analytics. Additional data processing steps may cleanse, correct, or enrich the final data sets. Transform: Data transformation involves converting data from its source schema, format, and other parameters to match the standards set for the destination. Extract: Data extraction is acquiring data from different sources, whether Oracle relational databases, Salesforce’s customer relationship management (CRM) system, or a real-time Internet of Things (IoT) data stream.

Etl extract transform and load the data